Update app.py

app.py

@@ -1,18 +1,19 @@
 import gradio as gr
-from langchain.llms import HuggingFacePipeline
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from langchain_community.llms import HuggingFacePipeline
+from langchain_core.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain_core.memory import ConversationBufferMemory
 
 # Load model and tokenizer
 model_name = "microsoft/DialoGPT-medium"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
-# Create HF pipeline
-pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=10000, do_sample=True)
+# Create text-generation pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=10000, do_sample=True, truncation=True)
 
-# Wrap with HuggingFacePipeline for LangChain
+# Wrap with HuggingFacePipeline
 llm = HuggingFacePipeline(pipeline=pipe)
 
 # Prompt Template
@@ -27,25 +28,30 @@ prompt = PromptTemplate(
 )
 
 # Memory
-memory = ConversationBufferMemory(memory_key="chat_history")
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
 
-# LLM Chain
+# Chain
 llm_chain = LLMChain(
     llm=llm,
     prompt=prompt,
     memory=memory,
+    verbose=True
 )
 
-# Response function
+# Chat function
 def get_text_response(user_message, history):
     response = llm_chain.predict(user_message=user_message)
     return response
 
-# Gradio Chat Interface
+# Gradio UI
 demo = gr.ChatInterface(
-    get_text_response,
-    examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"]
+    fn=get_text_response,
+    examples=["How are you doing?", "What are your interests?", "Which places do you like to visit?"],
+    title="AI Chatbot",
+    description="A simple chatbot using LangChain + HuggingFace + Gradio",
+    theme="default",
+    chatbot=gr.Chatbot(label="Assistant", show_label=True)
 )
 
-# Launch the app (no share=True needed for Spaces)
-demo.launch(share=True)
+if __name__ == "__main__":
+    demo.queue().launch(share=True)